Data & AI Modeler

Company Overview

Role Overview

Data Modelling & Architecture

• Design and maintain enterprise data models across core business domains, including customers, transactions, products, trading activity, events, financial data, and operational processes.
• Develop dimensional models using Kimball methodologies, including star schemas, snowflake schemas, and wide denormalised models tailored to different analytical and reporting needs.
• Design and implement Data Vault 2.0 models, including hubs, links, satellites, and business vault structures.
• Partner with Data Engineers to ensure data models are implemented efficiently and scalably across cloud data platforms such as Databricks, Snowflake, BigQuery, or Redshift.
• Ensure data models are extensible, well-documented, performant, and suitable for both BI and AI consumption.

Semantic Layer & Metric Governance

• Design and maintain governed semantic and metrics layers using dbt Metrics, LookML, or equivalent technologies.
• Establish canonical definitions for business metrics, KPIs, dimensions, hierarchies, calculations, and business logic.
• Ensure key business metrics are documented, auditable, consistently applied, and reused across BI dashboards, reporting, analytics, and AI systems.
• Work with BI and Analytics teams to expose governed datasets through reporting tools with appropriate access controls, definitions, and documentation.
• Reduce inconsistencies in reporting by creating a trusted, centralised data layer for enterprise consumption.

Ai & Machine Learning Data Architecture

• Design data foundations for machine learning and AI use cases, including reliable feature datasets for model training, validation, inference, and monitoring.
• Build point-in-time correct datasets that prevent data leakage and support reproducible model developme
• nt.Support feature store design and integration using technologies such as Feast, AWS SageMaker Feature Store, or custom feature platform solutions.
• Enable both offline historical feature generation and online, low-latency feature serving where required.
• Work closely with AI Engineers and Data Scientists to ensure training datasets, labels, features, and transformations are version controlled and production ready.

Data Quality, Governance & Lineage

• Implement automated data quality frameworks using dbt tests, Great Expectations, and related technologies.
• Define and monitor quality controls covering schema validation, null rates, referential integrity, freshness, duplication, reconciliation, and business-rule validation.
• Build and maintain end-to-end lineage documentation from source systems through transformation layers to BI, reporting, AI, and ML consumption.
• Maintain metadata, ownership, documentation, sensitivity classification, and governance standards across modelled datasets.
• Support the implementation and ongoing management of data cataloguing platforms such as OpenMetadata, Alation, Atlan, or equivalent tools.

Stakeholder Enablement

• Partner with Data Analysts, BI Engineers, AI Engineers, Product teams, and business stakeholders to understand data requirements and translate them into scalable model design.
• Provide clear documentation, training, and guidance that enables self-service access to trusted and well-modelled data.
• Explain data model decisions, metric definitions, and governance standards clearly to both technical and non-technical stakeholders.
• Promote data modelling, data governance, and semantic consistency best practices across the organisation.

Requirements

• 4–9 years of experience in Data Modelling, Data Warehousing, Data Architecture, Analytics Engineering, or a similar role.
• Strong practical experience designing dimensional models, including star schemas, snowflake schemas, fact tables, dimensions, and slowly changing dimensions.
• Hands-on experience with Data Vault 2.0 methodologies, including hubs, links, satellites, and business vault concepts.
• Advanced SQL skills with experience writing production-grade transformation logic.
• Strong experience with dbt, including models, macros, packages, tests, snapshots, documentation, and metrics.
• Experience designing and maintaining semantic layers, metrics layers, or governed analytical datasets for BI consumption.
• Understanding of machine learning data requirements, including feature engineering, feature stores, point-in-time correctness, temporal splits, data leakage prevention, and reproducible pipelines.
• Experience working with modern cloud data platforms such as Databricks, Snowflake, BigQuery, Redshift, or equivalent.
• Exposure to lakehouse and modern data storage formats, including Delta Lake, Apache Iceberg, and Parquet.
• Experience implementing data quality controls using dbt tests, Great Expectations, or similar tools.
• Familiarity with metadata management, data cataloguing, lineage, and governance platforms such as OpenMetadata, Alation, Atlan, OpenLineage, or Marquez.
• Strong stakeholder management and communication skills, with the ability to explain complex data concepts clearly to technical and business audiences.
• Experience within financial services, fintech, trading, payments, digital assets, or another high-volume transactional environment is highly preferred.

Core Technical Skills

• Data Modelling: Kimball methodology, star schema, snowflake schema, Data Vault 2.0, wide tables, semantic models
• Data Transformation: SQL, dbt, advanced macros, packages, snapshots, testing, documentation

• Technologies: Delta Lake, Apache Iceberg, Parquet

• Semantic & BI Layers: dbt Metrics, LookML, governed metrics layers, dimensional hierarchies
• Data Quality: Great Expectations, dbt tests, reconciliation, integrity validation, monitoring
• Governance & Metadata: OpenMetadata, Alation, Atlan, lineage, cataloguing, data ownership
• AI & ML Data Foundations: feature engineering, feature stores, Feast, SageMaker Feature Store, training datasets, inference datasets, data leakage prevention

What Sets This Role Apart

• Own the data foundation that supports enterprise-wide AI, analytics, reporting, and decision-making capabilities.
• Shape the modelling standards, semantic governance, and data quality practices used across the organisation.
• Work across both BI and AI/ML data architectures, creating a rare combination of analytical and AI enablement expertise.
• Play a critical role in ensuring AI systems, dashboards, and analytics products are built on trusted, scalable, and well-governed data.